Severity of traction bronchiectasis, when scored visually, is a powerful predictor of mortality in patients with fibrotic lung disease but technical challenges have made the development of automated methods for objective, reproducible quantification of this sign, difficult. We aimed to investigate the prognostic utility of a novel deep-learning algorithm for quantifying the severity of traction bronchiectasis in patients with idiopathic pulmonary fibrosis (IPF) enrolled in the Australian IPF Registry.
Walsh, S., Nan, Y., Humphries, S., Calandriello, L., Yang, G., Lynch, D., Wells, A., Corte, T., Utilising 3 Deep Learning Models for Outcome Prediction in Patients With Idiopathic Pulmonary Fibrosis, Abstract de <<ATS (AMERICAN THORACIC SOCIETY) CONGRESS 2024>>, (San Diego (CA), 17-22 May 2024 ), <<AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE>>, 2024; 209 (Supplement): 1-2 [https://hdl.handle.net/10807/324280]
Utilising 3 Deep Learning Models for Outcome Prediction in Patients With Idiopathic Pulmonary Fibrosis
Calandriello, Lucio;
2024
Abstract
Severity of traction bronchiectasis, when scored visually, is a powerful predictor of mortality in patients with fibrotic lung disease but technical challenges have made the development of automated methods for objective, reproducible quantification of this sign, difficult. We aimed to investigate the prognostic utility of a novel deep-learning algorithm for quantifying the severity of traction bronchiectasis in patients with idiopathic pulmonary fibrosis (IPF) enrolled in the Australian IPF Registry.
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